def test_cosmicray_median_ccddata():
ccd_data = ccd_data_func(data_scale=DATA_SCALE)
threshold = 5
add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
ccd_data.uncertainty = ccd_data.data * 0.0 + DATA_SCALE
nccd = cosmicray_median(ccd_data, thresh=5, mbox=11, error_image=None)
# check the number of cosmic rays detected
assert nccd.mask.sum() == NCRAYS
def test_cosmicray_median_background_None():
ccd_data = ccd_data_func(data_scale=DATA_SCALE)
threshold = 5
add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
data, crarr = cosmicray_median(ccd_data.data,
thresh=5,
mbox=11,
error_image=None)
# check the number of cosmic rays detected
assert crarr.sum() == NCRAYS
def test_cosmicray_median_does_not_change_input():
ccd_data = ccd_data_func()
original = ccd_data.copy()
error = np.zeros_like(ccd_data)
ccd = cosmicray_median(ccd_data,
error_image=error,
thresh=5,
mbox=11,
gbox=0,
rbox=0)
np.testing.assert_array_equal(original.data, ccd_data.data)
assert original.unit == ccd_data.unit
def test_cosmicray_median_masked():
ccd_data = ccd_data_func(data_scale=DATA_SCALE)
threshold = 5
add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
data = np.ma.masked_array(ccd_data.data, (ccd_data.data > -1e6))
ndata, crarr = cosmicray_median(data,
thresh=5,
mbox=11,
error_image=DATA_SCALE)
# check the number of cosmic rays detected
assert crarr.sum() == NCRAYS
def test_cosmicray_median_does_not_change_input():
ccd_data = ccd_data_func()
original = ccd_data.copy()
error = np.zeros_like(ccd_data)
with np.errstate(invalid="ignore", divide="ignore"):
_ = cosmicray_median(ccd_data,
error_image=error,
thresh=5,
mbox=11,
gbox=0,
rbox=0)
np.testing.assert_array_equal(original.data, ccd_data.data)
assert original.unit == ccd_data.unit
def test_cosmicray_median_rbox():
ccd_data = ccd_data_func(data_scale=DATA_SCALE)
scale = DATA_SCALE # yuck. Maybe use pytest.parametrize?
threshold = 5
add_cosmicrays(ccd_data, scale, threshold, ncrays=NCRAYS)
error = ccd_data.data * 0.0 + DATA_SCALE
data, crarr = cosmicray_median(ccd_data.data,
error_image=error,
thresh=5,
mbox=11,
rbox=21,
gbox=5)
assert data[crarr].mean() < ccd_data.data[crarr].mean()
assert crarr.sum() > NCRAYS
def test_cosmicray_median_gbox():
ccd_data = ccd_data_func(data_scale=DATA_SCALE)
scale = DATA_SCALE # yuck. Maybe use pytest.parametrize?
threshold = 5
add_cosmicrays(ccd_data, scale, threshold, ncrays=NCRAYS)
error = ccd_data.data * 0.0 + DATA_SCALE
data, crarr = cosmicray_median(ccd_data.data,
error_image=error,
thresh=5,
mbox=11,
rbox=0,
gbox=5)
data = np.ma.masked_array(data, crarr)
assert crarr.sum() > NCRAYS
assert abs(data.std() - scale) < 0.1
def test_cosmicray_median_background_deviation():
ccd_data = ccd_data_func(data_scale=DATA_SCALE)
with pytest.raises(TypeError):
cosmicray_median(ccd_data.data, thresh=5, mbox=11, error_image='blank')
def test_cosmicray_median_check_data():
with pytest.raises(TypeError):
ndata, crarr = cosmicray_median(10,
thresh=5,
mbox=11,
error_image=DATA_SCALE)
